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Differentially Private Synthetic Data: Applied Evaluations and Enhancements
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Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models. Differentially private data synthesis protects personal details from exposure, and allows for the training of differentially private machine learning models on privately generated datasets. But how can we effectively assess the efficacy of differentially private synthetic data? In this paper, we survey four differentially private generative adversarial networks for data synthesis. We evaluate each of them at scale on five standard tabular datasets, and in two applied industry scenarios. We benchmark with novel metrics from recent literature and other standard machine learning tools. Our results suggest some synthesizers are more applicable for different privacy budgets, and we further demonstrate complicating domain-based tradeoffs in selecting an approach. We offer experimental learning on applied machine learning scenarios with private internal data to researchers and practioners alike. In addition, we propose QUAIL, an ensemble-based modeling approach to generating synthetic data. We examine QUAIL's tradeoffs, and note circumstances in which it outperforms baseline differentially private supervised learning models under the same budget constraint.
Forward citations
Cited by 7 Pith papers
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Struct-Bench: A Benchmark for Differentially Private Structured Text Generation
Struct-Bench is a Context-Free Grammar based evaluation platform showing that current differentially private synthetic text generators often fail to preserve structure and semantic diversity.
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Private Training & Data Generation by Clustering Embeddings
A DP pipeline that clusters CLIP embeddings, privately fits a Gaussian mixture model, and samples new embeddings achieves SOTA private classification on CIFAR-10 and CAMELYON17.
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Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
LLM-generated surrogate public data, built from schema metadata alone, can substitute for traditional public data when pretraining differentially private tabular classifiers in small-data settings.
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Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
Interviews with 17 data experts show skepticism toward differentially private synthetic data, a last-resort stance, and a demand for validation against real data.
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Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
Fairness metrics computed on differentially private synthetic data differ from real-data values by up to 0.32 for some measures, despite staying below 0.1 on average, across Adult, COMPAS, and Diabetes.
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SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
A study of an LLM-based synthetic data pipeline with noise injection that claims differential privacy, evaluated on news classification, but with no valid privacy analysis.
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How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
A practical, extremely thorough survey of differentially private synthetic data generation: methods, privacy units, evaluation metrics, and end-to-end system components across four data modalities.
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